Download or read book Deep Learning Based Algorithms for Object Tracking and Dolphin Detection and Identification written by 許宏瑋 and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Visual Object Tracking with Deep Neural Networks written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2019-12-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Download or read book Advancement of Deep Learning and its Applications in Object Detection and Recognition written by Roohie Naaz Mir and published by CRC Press. This book was released on 2023-05-10 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.

Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.

Download or read book Deep Learning in Object Detection and Recognition written by Xiaoyue Jiang and published by Springer. This book was released on 2020-11-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by Springer Nature. This book was released on 2021-11-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Download or read book Visual Object Tracking using Deep Learning written by Ashish Kumar and published by CRC Press. This book was released on 2023-11-10 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods. Elaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexity. Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios. Explores the future research directions for visual tracking by analyzing the real-time applications. The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Download or read book Deep Learning for Crack Like Object Detection written by Kaige Zhang and published by CRC Press. This book was released on 2023-03-20 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Download or read book Deep Learning Based Multi modal Perception and Semi automatic Labelling Algorithms for Automotive Sensor Data written by Christian Haase-Schütz and published by BoD – Books on Demand. This book was released on 2023 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Based Computer Vision for Animal Re Identification written by Stefan Schneider and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Animal re-identification (re-ID) is fundamental to our understanding of community ecology, population dynamics and ethological analyses. Recent advances in the area of deep learning for computer vision offer a promising solution to improve upon the current methodologies for animal re-ID. The success of deep learning methods for human re-ID is well documented when ample training images are available for each individual. Despite this success, little has been done utilizing their capabilities for animal re-ID. In order to implement animal re-ID systems in practice, deep learning systems must be able to accomplish a variety of computer vision objectives. These include: quantifying the number of animals in an image, classifying the animal species within an image, localizing and extracting animal individuals within an image, and lastly re-identifying animal individuals. This work begins with a review of computer vision methods for animal re-ID (Chapter 2). I explore the quantification of animal individuals from images considering fish and dolphin counts in the Amazon River (Chapter 3). I then demonstrate the success of deep learning methods considering species identification, strategies for handling class imbalance, and quantifying performance when testing on background locations that are included/excluded from training (Chapter 4). I demonstrate the ability of deep learning systems to classify and localize animal species from camera trap images considering three global environments (Chapter 5). I then utilize five animal individual data sets to compare the success and generality of similarity comparison deep learning methods for animal re-ID (Chapter 6). Finally, I demonstrate these techniques in combination to successfully implement animal re-ID for an entirely novel study of Octopus tetricus social behaviour (Chapter 7). This work describes the complete animal re-ID pipeline for ecologists to follow in practice, outlining expected accuracies and guidelines for best practices. It imprints results to the machine learning research community considering tasks relative to the under represented task of animal re-ID. This work provides details on the necessary components required to achieve real-time camera trap survey systems. Lastly, this work encourages the progress of interdisciplinary areas of science.

Download or read book 2023 Asia Pacific International Symposium on Aerospace Technology APISAT 2023 Proceedings written by Song Fu and published by Springer Nature. This book was released on with total page 1991 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Deep Learning based Object Detection Algorithms for Omnidirectional Images by Simulated Data written by Tobias Scheck and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book DEEP LEARNING BASED OBJECT PERCEPTION ALGORITHM AND APPLICATION written by Fan Yang and published by . This book was released on 2020 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object perception as a fundamental task in computer vision has a broad of applicationin real word, such as self-driving, industrial defect inspection, intelligent agriculture. Numerous works have been studied to advance the progress of object perception. In particular, due to the powerful feature learning and representation ability of deep learning, object perception algorithm has achieved signicant progress. In the dissertation, I rst introduce the denition of object perception and its three subtasks: object detection, pose estimation, and object segmentation; then specically present our works in the three subtasks, respectively. Object detection: we conduct two works, clustered object detection in aerial image (ClusDet) and dually supervised feature pyramid for object detection and segmentation (DSFPN). The ClusDet is designed to leverage the prior that objects in aerial ( especially in trac scenario) tend to cluster in dierent scales for object detection. By comparing with evenly crop method, ClusDet can achieve superior precision with less computation load. The DSFPN is proposed to alleviate the gradient degradation or vanishing problem in feature pyramid network (FPN) for object detection and segmentation. In particular, we note that performance of the two-stage detectors do not constantly increase with the growing complexity of backbone network, which is consistent with the conclusion in \deep residual learning for image recognition". To mitigate the problem, we propose to add extra supervision signal on bottom-up path of FPN in training phase to enhance the gradient information so as to facilitate the model training. Pose estimation: a robust dynamic fusion (RDF) algorithm is proposed to deal with noisy modalities in patient body modeling. In particular, for patient body modeling, the RGB camera cannot provide sucient information because of the body covered with blanket or loosen cloth. In this case, multi-modality (e.g., RGB, thermal, depth) sensors are required to acquire complimentary information. However, dierent application may need dierent sensors. It is labor-intensive and time-consuming to train a model per an application. In addition, multi-modality images may come to various noise in deployment, so that the trained model fails to work precisely. To deal with the aforementioned issues, we propose the RDF in conjunction with a dynamic training strategy to adaptively depress the features from noisy modalities, such that the model can be trained once and deployed any of the modalities. Object segmentation: the object here refers to crack, we propose a feature pyramid and hierarchical boosting network (FPHBN) for pavement crack detection. Specically, the crack in pavement has various scales (width), based on this characteristic, we introduce a feature pyramid architecture to utilize the inherent hierarchy of deep convolution networks (DConvNets) to construct multi-scale features for multi-scale cracks. Beside, each layer of the DConvNets is not independent, to leverage this dependency, we design a hierarchical boosting module to reweight samples via the prediction from adjunct layer. With the benet of the boosting module, the proposed network can dynamically pay more attention to hard samples.

Download or read book Multiple Object Tracking Using Deep Learning Techniques written by Laia Prat Ortonobas and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This project has been devoted to (i) learning what Multiple Object Tracking (MOT) is, (ii) learning Python, one of the most used languages in Machine Learning and computer vision, and (iii) to evaluate a tracker (TrajTrack), currently being developed at the image processing group (GPI), against the UA-DETRAC dataset. The work has been divided in two parts. On the one hand, we have studied MOT and its main challenges, such as occlusions or identity switches, in order to follow multiple objects throughout a video sequence. To fully understand this problem, we have developed a multiple tennis ball tracker in Python from scratch. On the other hand, we have used TrajTrack, which is evaluated on a pedestrian dataset (MOT17), and adapted it to be evaluated against a car dataset (UA-DETRAC). For this, we have retrained the detection and re-identification models. We have obtained a 98.6% MOTA score for training and a 74.7% MOTA score for testing. These results are comparable with the state-of-the-art techniques.